is a set of self-supervised techniques which consider that each image corresponds to its own class [1]}, [2]}.
A set of data augmentations (or transformations) is then applied to each image to generate multiple examples for each class.
The global image representations are trained in a contrastive framework, typically using the InfoNCE loss [3]}, to have high similarity for instances transformed from the same source image and low similarity with all other images.
As the performance of these methods depends on the number of negatives, it either requires large batches or memory banks to work well [3]}, [5]}, [6]}.
It was later shown that when using a momentum encoder [5]}, simpler loss functions that did not directly discriminate against other images could be used [8]}, [9]}, [10]}, [11]}, [12]}.
Finally, a related line of work is to use clustering techniques to pre-train deep neural networks [13]}, [14]}, [15]}, [16]}, [17]}.
